The progress in genome sequencing projects has generated a large number of inferred protein sequences from different organisms and, it is likely to increase in the coming years. The availability of complete genome sequences offers an opportunity for increased understanding of the biology of these organisms because it not only provides biological insights on any given organism, but also provides substantially more information on the physiology and evolution of microbial species through comparative analysis (Fraser et al. 2000). The set of microbes whose genomes have been sequenced so far is a diverse one, ranging from organisms living under extreme condition of environment to model organisms of biology, and to some of the most important human pathogens (see, U.S. National Center for Biotechnology Information, U.S. National Institutes of Health, website).
It is expected that the availability of the information on the complete set of proteins from the infectious human pathogens will enable us to develop novel drugs to combat them. This is important in cases such as the emerging epidemic of multiple drug-resistant Mycobacterial isolates (Barry et al. 2000) although, so far, no new drugs derived from genomics-based discovery have been reported to be in a development pipeline (Black and Hare 2000). A paradigm for exploiting the genome to inform the development of novel antituberculars has been proposed, utilizing the techniques of differential gene expression as monitored by DNA microarrays coupled with the emerging discipline of combinatorial chemistry (Barry et al. 2000).
The whole genome sequences of microbial pathogens also present new opportunities for clinical applications such as diagnostics and vaccines (Weinstock et al. 2000). However, the predicted number of proteins encoded in different genomes is fairly large, and about half of that in any given genome is of unknown biological function (Fraser et al. 2000). Some of them are also unique in each organism. In this scenario, development of data mining tools and their application to decipher useful patterns in the protein sequence dataset can be useful for suitable experiments such as differential gene expression, heterologous expression for large-scale (Weinstock et al 2000) and proteomics studies (Chakravarti 2000). Recently, it has been demonstrated that utilization of genome sequences by application of bioinformatics through genomics and proteomics can expedite the vaccine discovery process by rapidly providing a set of potential candidates for further testing (Chakravarti 2000 (a) and (b)). Presently data mining is being carried out using traditional computer programs that perform motif search or identify distinct domains differing in physico-chemical properties such as hydrophobicity, sequence conservation. The drawback of these methods is that the functions of a half to one third number of the proteins remain unknown even after their applications. Therefore, through the application of the presently available computation tools it is likely that potential new candidate for vaccines, diagnostics or drug targets are missed. Therefore, need exists for development of a computational tool that uses different sequence attributes of protein sequences instead of sequence patterns. Through such a shift in framework, the applicants have overcome this limitation. The novelty of the present invention is in development of method based on different attributes of protein sequences, which is useful for prediction of functional role in virulence, immuno-pathogenicity and drug-response.